In Fig. REF , Fig. REF , Fig. REF , Fig. REF , and Fig. REF  we provide some object removal instances in large images from 1k to 2k resolutions compared with LaMa [1]}. Some cases are selected from the open-source testset of LaMa blackand others are selected from our HR-Flickr. Note that our method outperforms LaMa in scenes with weak textures such as row 2 in Fig. REF  and row 1 in Fig. REF . For the cases with sparse regular textures and lines (rows 1,3 of Fig. REF ), our method can still achieve more clear borderlines. For the cases with dense regular textures (rows 2,3 of Fig. REF ), LaMa gets competitive results, which shows that FFC in frequency fields has solved these problems properly. However, our method can also achieve results with less blur that benefited from precise structural constraints. For the larger case with 2048 image size in Fig. REF , our method can still get more consistent result compared with LaMa.
